{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Action1_ARIMA预测美吉姆股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "# 沪指数走势预测，使用时间序列ARIMA\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "import statsmodels.api as sm\n",
    "import warnings\n",
    "from itertools import product\n",
    "from datetime import datetime, timedelta\n",
    "import calendar\n",
    "\n",
    "warnings.filterwarnings('ignore') # 忽略报错\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示matplotlib图中的中文"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020/10/16</td>\n",
       "      <td>7.00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020/10/15</td>\n",
       "      <td>7.02</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020/10/14</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020/10/13</td>\n",
       "      <td>6.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020/10/12</td>\n",
       "      <td>6.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>2011/10/12</td>\n",
       "      <td>23.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2192</th>\n",
       "      <td>2011/10/11</td>\n",
       "      <td>24.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2193</th>\n",
       "      <td>2011/10/10</td>\n",
       "      <td>22.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2194</th>\n",
       "      <td>2011/9/30</td>\n",
       "      <td>23.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>2011/9/29</td>\n",
       "      <td>23.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2196 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Timestamp  Price\n",
       "0     2020/10/16   7.00\n",
       "1     2020/10/15   7.02\n",
       "2     2020/10/14   6.93\n",
       "3     2020/10/13   6.95\n",
       "4     2020/10/12   6.99\n",
       "...          ...    ...\n",
       "2191  2011/10/12  23.60\n",
       "2192  2011/10/11  24.25\n",
       "2193  2011/10/10  22.66\n",
       "2194   2011/9/30  23.10\n",
       "2195   2011/9/29  23.98\n",
       "\n",
       "[2196 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据加载\n",
    "df = pd.read_csv('./002621.csv')\n",
    "df = df[['日期', '开盘价']]\n",
    "df.rename(columns = {\"日期\": \"Timestamp\", \"开盘价\":\"Price\"},  inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>6.95</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020/10/12</td>\n",
       "      <td>6.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>2191</th>\n",
       "      <td>2011/10/12</td>\n",
       "      <td>23.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2192</th>\n",
       "      <td>2011/10/11</td>\n",
       "      <td>24.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2193</th>\n",
       "      <td>2011/10/10</td>\n",
       "      <td>22.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2194</th>\n",
       "      <td>2011/9/30</td>\n",
       "      <td>23.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>2011/9/29</td>\n",
       "      <td>23.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2196 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Timestamp  Price\n",
       "0     2020/10/16   7.00\n",
       "1     2020/10/15   7.02\n",
       "2     2020/10/14   6.93\n",
       "3     2020/10/13   6.95\n",
       "4     2020/10/12   6.99\n",
       "...          ...    ...\n",
       "2191  2011/10/12  23.60\n",
       "2192  2011/10/11  24.25\n",
       "2193  2011/10/10  22.66\n",
       "2194   2011/9/30  23.10\n",
       "2195   2011/9/29  23.98\n",
       "\n",
       "[2196 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 去掉0值\n",
    "df = df.replace(0,np.nan)\n",
    "# df.dropna(inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Price</th>\n",
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       "      <th>Timestamp</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>2020-10-16</th>\n",
       "      <td>2020-10-16</td>\n",
       "      <td>7.00</td>\n",
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       "    <tr>\n",
       "      <th>2020-10-15</th>\n",
       "      <td>2020-10-15</td>\n",
       "      <td>7.02</td>\n",
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       "    <tr>\n",
       "      <th>2020-10-14</th>\n",
       "      <td>2020-10-14</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-13</th>\n",
       "      <td>2020-10-13</td>\n",
       "      <td>6.95</td>\n",
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       "    <tr>\n",
       "      <th>2020-10-12</th>\n",
       "      <td>2020-10-12</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-12</th>\n",
       "      <td>2011-10-12</td>\n",
       "      <td>23.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-11</th>\n",
       "      <td>2011-10-11</td>\n",
       "      <td>24.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-10</th>\n",
       "      <td>2011-10-10</td>\n",
       "      <td>22.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-30</th>\n",
       "      <td>2011-09-30</td>\n",
       "      <td>23.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-29</th>\n",
       "      <td>2011-09-29</td>\n",
       "      <td>23.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2196 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Timestamp  Price\n",
       "Timestamp                   \n",
       "2020-10-16 2020-10-16   7.00\n",
       "2020-10-15 2020-10-15   7.02\n",
       "2020-10-14 2020-10-14   6.93\n",
       "2020-10-13 2020-10-13   6.95\n",
       "2020-10-12 2020-10-12   6.99\n",
       "...               ...    ...\n",
       "2011-10-12 2011-10-12  23.60\n",
       "2011-10-11 2011-10-11  24.25\n",
       "2011-10-10 2011-10-10  22.66\n",
       "2011-09-30 2011-09-30  23.10\n",
       "2011-09-29 2011-09-29  23.98\n",
       "\n",
       "[2196 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将时间作为df的索引\n",
    "df.Timestamp = pd.to_datetime(df.Timestamp)\n",
    "df.index = df.Timestamp\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Price</th>\n",
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       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
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       "      <th>2011-09-30</th>\n",
       "      <td>23.540000</td>\n",
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       "    <tr>\n",
       "      <th>2011-10-31</th>\n",
       "      <td>24.991875</td>\n",
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       "    <tr>\n",
       "      <th>2011-11-30</th>\n",
       "      <td>26.670909</td>\n",
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       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>23.110909</td>\n",
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       "    <tr>\n",
       "      <th>2012-01-31</th>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>7.238500</td>\n",
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       "    <tr>\n",
       "      <th>2020-07-31</th>\n",
       "      <td>7.068261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31</th>\n",
       "      <td>7.261429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>6.767273</td>\n",
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       "    <tr>\n",
       "      <th>2020-10-31</th>\n",
       "      <td>6.936667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Price\n",
       "Timestamp            \n",
       "2011-09-30  23.540000\n",
       "2011-10-31  24.991875\n",
       "2011-11-30  26.670909\n",
       "2011-12-31  23.110909\n",
       "2012-01-31  20.176667\n",
       "...               ...\n",
       "2020-06-30   7.238500\n",
       "2020-07-31   7.068261\n",
       "2020-08-31   7.261429\n",
       "2020-09-30   6.767273\n",
       "2020-10-31   6.936667\n",
       "\n",
       "[110 rows x 1 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df按天\n",
    "# 按照月，季度，年来统计\n",
    "df_month = df.resample('M').mean()\n",
    "df_Q = df.resample('Q-DEC').mean()\n",
    "df_year = df.resample('A-DEC').mean()\n",
    "df_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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    {
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       "      <th>...</th>\n",
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       "      <th>2020-07-31</th>\n",
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       "    <tr>\n",
       "      <th>2020-08-31</th>\n",
       "      <td>7.261429</td>\n",
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       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>6.767273</td>\n",
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       "    <tr>\n",
       "      <th>2020-10-31</th>\n",
       "      <td>6.936667</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Price\n",
       "Timestamp            \n",
       "2011-09-30  23.540000\n",
       "2011-10-31  24.991875\n",
       "2011-11-30  26.670909\n",
       "2011-12-31  23.110909\n",
       "2012-01-31  20.176667\n",
       "...               ...\n",
       "2020-06-30   7.238500\n",
       "2020-07-31   7.068261\n",
       "2020-08-31   7.261429\n",
       "2020-09-30   6.767273\n",
       "2020-10-31   6.936667\n",
       "\n",
       "[103 rows x 1 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这里不能dropna，否则后面预测会为空值\n",
    "# df_month.dropna(inplace=True)\n",
    "# df_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2196 entries, 2020-10-16 to 2011-09-29\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Price   1963 non-null   float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 34.3 KB\n"
     ]
    }
   ],
   "source": [
    "df[['Price']].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.2146259747095813,\n",
       " 0.20097996142682156,\n",
       " 3,\n",
       " 99,\n",
       " {'1%': -3.498198082189098,\n",
       "  '5%': -2.891208211860468,\n",
       "  '10%': -2.5825959973472097},\n",
       " 386.0978397996492)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 进行假设检验-->adf检验\n",
    "# from statsmodels.tsa.stattools import adfuller\n",
    "# adf_result = adfuller(df_month.dropna().Price)\n",
    "# adf_result  # 0.2未拒绝，趋势太明显，需要差分平了它-->目标是0.05以下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ARMA找p、q\n",
    "# 这里df_month不能有空行\n",
    "# import warnings\n",
    "# warnings.filterwarnings('ignore')\n",
    "# import statsmodels.api as stm\n",
    "# ic_result = stm.tsa.arma_order_select_ic(df_month.Price,max_ar=5,max_ma=5,ic=['aic'])   #阶选择器,选择出 ARMA yt中最合适的的p,q值\n",
    "# ic_result.aic_min_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Price</td>      <th>  No. Observations:  </th>    <td>103</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARMA(1, 0)</td>    <th>  Log Likelihood     </th> <td>-230.539</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>2.249</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Tue, 20 Oct 2020</td> <th>  AIC                </th>  <td>467.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>19:54:10</td>     <th>  BIC                </th>  <td>474.983</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>0</td>        <th>  HQIC               </th>  <td>470.280</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>       <td>   16.0753</td> <td>    2.472</td> <td>    6.503</td> <td> 0.000</td> <td>   11.230</td> <td>   20.921</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.Price</th> <td>    0.9188</td> <td>    0.040</td> <td>   23.036</td> <td> 0.000</td> <td>    0.841</td> <td>    0.997</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>           1.0884</td> <td>          +0.0000j</td> <td>           1.0884</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                  Price   No. Observations:                  103\n",
       "Model:                     ARMA(1, 0)   Log Likelihood                -230.539\n",
       "Method:                       css-mle   S.D. of innovations              2.249\n",
       "Date:                Tue, 20 Oct 2020   AIC                            467.079\n",
       "Time:                        19:54:10   BIC                            474.983\n",
       "Sample:                             0   HQIC                           470.280\n",
       "                                                                              \n",
       "===============================================================================\n",
       "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "const          16.0753      2.472      6.503      0.000      11.230      20.921\n",
       "ar.L1.Price     0.9188      0.040     23.036      0.000       0.841       0.997\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            1.0884           +0.0000j            1.0884            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# p，q带入ARMA模型--\n",
    "# from statsmodels.tsa.arima_model import ARMA \n",
    "# best_model=ARMA(df_month.Price,order=(1,0)).fit()\n",
    "# best_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置参数范围\n",
    "ps = range(0, 5)\n",
    "qs = range(0, 5)\n",
    "ds = range(1, 2)\n",
    "parameters = product(ps, ds, qs)\n",
    "parameters_list = list(parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 1, 0),\n",
       " (0, 1, 1),\n",
       " (0, 1, 2),\n",
       " (0, 1, 3),\n",
       " (0, 1, 4),\n",
       " (1, 1, 0),\n",
       " (1, 1, 1),\n",
       " (1, 1, 2),\n",
       " (1, 1, 3),\n",
       " (1, 1, 4),\n",
       " (2, 1, 0),\n",
       " (2, 1, 1),\n",
       " (2, 1, 2),\n",
       " (2, 1, 3),\n",
       " (2, 1, 4),\n",
       " (3, 1, 0),\n",
       " (3, 1, 1),\n",
       " (3, 1, 2),\n",
       " (3, 1, 3),\n",
       " (3, 1, 4),\n",
       " (4, 1, 0),\n",
       " (4, 1, 1),\n",
       " (4, 1, 2),\n",
       " (4, 1, 3),\n",
       " (4, 1, 4)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最优模型:                                 SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                  Price   No. Observations:                  110\n",
      "Model:               SARIMAX(2, 1, 4)   Log Likelihood                -207.814\n",
      "Date:                Tue, 20 Oct 2020   AIC                            429.627\n",
      "Time:                        20:12:12   BIC                            448.138\n",
      "Sample:                    09-30-2011   HQIC                           437.127\n",
      "                         - 10-31-2020                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1         -0.5841      0.198     -2.950      0.003      -0.972      -0.196\n",
      "ar.L2         -0.6810      0.125     -5.443      0.000      -0.926      -0.436\n",
      "ma.L1          0.7208      0.228      3.163      0.002       0.274       1.168\n",
      "ma.L2          0.9383      0.174      5.388      0.000       0.597       1.280\n",
      "ma.L3          0.0805      0.132      0.609      0.542      -0.178       0.339\n",
      "ma.L4          0.0435      0.126      0.346      0.729      -0.203       0.290\n",
      "sigma2         3.9805      0.402      9.912      0.000       3.193       4.768\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):               157.73\n",
      "Prob(Q):                              0.97   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               1.01   Skew:                            -1.39\n",
      "Prob(H) (two-sided):                  0.99   Kurtosis:                         8.36\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "# 寻找最优ARIMA模型参数，即best_aic最小\n",
    "results = []\n",
    "best_aic = float(\"inf\") # 正无穷\n",
    "for param in parameters_list:\n",
    "    try:\n",
    "        #model = ARIMA(df_month.Price,order=(param[0], param[1], param[2])).fit()\n",
    "        # SARIMAX 包含季节趋势因素的ARIMA模型\n",
    "        model = sm.tsa.statespace.SARIMAX(df_month.Price,  \n",
    "                                order=(param[0], param[1], param[2]),\n",
    "                                #seasonal_order=(4, 1, 2, 12),\n",
    "                                enforce_stationarity=False,\n",
    "                                enforce_invertibility=False).fit()\n",
    "\n",
    "    except ValueError:\n",
    "        print('参数错误:', param)\n",
    "        continue\n",
    "    aic = model.aic\n",
    "    if aic < best_aic:\n",
    "        best_model = model\n",
    "        best_aic = aic\n",
    "        best_param = param\n",
    "    results.append([param, model.aic])\n",
    "# 输出最优模型\n",
    "print('最优模型: ', best_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_list= [Timestamp('2020-11-30 00:00:00', freq='M'), Timestamp('2020-12-31 00:00:00', freq='M'), Timestamp('2021-01-31 00:00:00', freq='M')]\n"
     ]
    }
   ],
   "source": [
    "# 设置future_month，需要预测的时间date_list\n",
    "# 2020-11-30\tNaN\n",
    "# 2020-12-31\tNaN\n",
    "# 2021-01-31\tNaN\n",
    "df_month2 = df_month[['Price']]\n",
    "future_month = 3\n",
    "last_month = pd.to_datetime(df_month2.index[len(df_month2)-1])\n",
    "df_month2\n",
    "date_list = []\n",
    "for i in range(future_month):\n",
    "    # 计算下个月有多少天\n",
    "    year = last_month.year\n",
    "    month = last_month.month\n",
    "    if month == 12:\n",
    "        month = 1\n",
    "        year = year+1\n",
    "    else:\n",
    "        month = month + 1\n",
    "    next_month_days = calendar.monthrange(year, month)[1]\n",
    "    #print(next_month_days)\n",
    "    last_month = last_month + timedelta(days=next_month_days)\n",
    "    date_list.append(last_month)\n",
    "print('date_list=', date_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-09-30</th>\n",
       "      <td>23.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-31</th>\n",
       "      <td>24.991875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-30</th>\n",
       "      <td>26.670909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>23.110909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-31</th>\n",
       "      <td>20.176667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>6.767273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31</th>\n",
       "      <td>6.936667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-31</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Price\n",
       "2011-09-30  23.540000\n",
       "2011-10-31  24.991875\n",
       "2011-11-30  26.670909\n",
       "2011-12-31  23.110909\n",
       "2012-01-31  20.176667\n",
       "...               ...\n",
       "2020-09-30   6.767273\n",
       "2020-10-31   6.936667\n",
       "2020-11-30        NaN\n",
       "2020-12-31        NaN\n",
       "2021-01-31        NaN\n",
       "\n",
       "[113 rows x 1 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 添加未来要预测的3个月\n",
    "future = pd.DataFrame(index=date_list, columns= df_month.columns)\n",
    "df_month2 = pd.concat([df_month2, future])\n",
    "df_month2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get_prediction得到的是区间，使用predicted_mean,第一个设置值为0\n",
    "df_month2['forecast'] = best_model.get_prediction(start=0, end=len(df_month2)).predicted_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-09-30</th>\n",
       "      <td>23.540000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-31</th>\n",
       "      <td>24.991875</td>\n",
       "      <td>16.664609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-30</th>\n",
       "      <td>26.670909</td>\n",
       "      <td>17.543167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>23.110909</td>\n",
       "      <td>24.701423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-31</th>\n",
       "      <td>20.176667</td>\n",
       "      <td>24.047041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>6.767273</td>\n",
       "      <td>7.432981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31</th>\n",
       "      <td>6.936667</td>\n",
       "      <td>6.634274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.795396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.004314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.973849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Price   forecast\n",
       "2011-09-30  23.540000   0.000000\n",
       "2011-10-31  24.991875  16.664609\n",
       "2011-11-30  26.670909  17.543167\n",
       "2011-12-31  23.110909  24.701423\n",
       "2012-01-31  20.176667  24.047041\n",
       "...               ...        ...\n",
       "2020-09-30   6.767273   7.432981\n",
       "2020-10-31   6.936667   6.634274\n",
       "2020-11-30        NaN   6.795396\n",
       "2020-12-31        NaN   7.004314\n",
       "2021-01-31        NaN   6.973849\n",
       "\n",
       "[113 rows x 2 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 预测结果显示\n",
    "plt.figure(figsize=(30,7))\n",
    "df_month2.Price.plot(label='实际指数')\n",
    "df_month2.forecast.plot(color='r', ls='--', label='预测指数')\n",
    "plt.legend()\n",
    "plt.title('指数（月）')\n",
    "plt.xlabel('时间')\n",
    "plt.ylabel('指数')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.0 64-bit ('Bi_env': venv)",
   "language": "python",
   "name": "python38064bitbienvvenvba07af95a1bb4b078aa8134bba84dff2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
